Description Usage Arguments Details Value Author(s) Examples

This function computes polygenic risk scores from the estimates of latent factor models. It uses the indirect' effect sizes for the regression of X (a single phenotype) on the matrix Y, for predicting phenotypic values for new genotype data.

1 | ```
predict_lfmm(Y, X, lfmm.object, fdr.level = 0.1, newdata = NULL)
``` |

`Y` |
a response variable matrix with n rows and p columns, typically containing genotypes. Each column is a response variable (numeric). |

`X` |
an explanatory variable with n rows and d = 1 column (numeric) representing a phenotype with zero mean across the sample. |

`lfmm.object` |
an object of class |

`fdr.level` |
a numeric value for the FDR level in the lfmm test used to define candidate variables for predicting new phenotypes. |

`newdata` |
a matrix with n rows and p' columns, and similar to Y, on which predictions of X will be based. If NULL, Y is used as new data. |

The response variable matrix Y and the explanatory variable are centered.

a list with the following attributes:

prediction: a vector of length n containing the predicted values for X. If newdata = NULL, the fitted values are returned.

candidates: a vector of candidate columns of Y on which the predictions are built.

cayek, francoio

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | ```
library(lfmm)
## Simulation of 1000 genotypes for 100 individuals (y)
u <- matrix(rnorm(300, sd = 1), nrow = 100, ncol = 2)
v <- matrix(rnorm(3000, sd = 2), nrow = 2, ncol = 1000)
y <- matrix(rbinom(100000, size = 2,
prob = 1/(1 + exp(-0.3*(u%*%v
+ rnorm(100000, sd = 2))))),
nrow = 100,
ncol = 1000)
#PCA of genotypes, 2 main axes of variation (K = 2)
plot(prcomp(y))
## Simulation of 1000 phenotypes (x)
## Only the last 10 genotypes have significant effect sizes (b)
b <- matrix(c(rep(0, 990), rep(6000, 10)))
x <- y%*%b + rnorm(100, sd = 100)
## Compute effect sizes using lfmm_ridge
mod <- lfmm_ridge(Y = y,
X = x,
K = 2)
x.pred <- predict_lfmm(Y = y,
X = x,
fdr.level = 0.25,
mod)
x.pred$candidates
##Compare simulated and predicted/fitted phenotypes
plot(x - mean(x), x.pred$pred,
pch = 19, col = "grey",
xlab = "Observed phenotypes (centered)",
ylab = "Predicted from PRS")
abline(0,1)
abline(lm(x.pred$pred ~ scale(x, scale = FALSE)), col = 2)
``` |

cayek/MatrixFactorizationR documentation built on Feb. 19, 2018, 2:04 p.m.

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